Modelling the implied volatility surface based on Shanghai 50ETF options

We develop a dynamic factor model to forecast the implied volatility surface (IVS) of Shanghai Stock Exchange 50ETF options. Based on the assumption that dynamic change in IVS is mean-reverting and Markovian, we use a state space model to capture the dynamics of IVS, and set the latent factors to be the Ornstein–Uhlenbeck processes. We obtain the optimal estimations of parameters using the Kalman filter algorithm. Empirical results show that our model performs better than the traditional IVS model in terms of fitting ability and prediction performance.     

Political uncertainty and a firm's credit risk: Evidence from the international CDS market

Using a large sample of firms with single-name credit default swap (CDS) contracts in 30 countries, we document the evidence that political uncertainty, proxied by national election dummy, is positively related to firm-level credit risk. Specifically, this positive relation is more pronounced for the firms that have no political connection or poor international diversification, and in the countries with higher political uncertainty and lower investor protections. Further, by using a difference-in-differences approach, we find evidence to support idiosyncratic volatility and debt rollover channels through which political uncertainty affects the credit risk of individual firm.     

Short-time at-the-money skew and rough fractional volatility

The Black–Scholes implied volatility skew at the money of SPX options is known to obey a power law with respect to the time to maturity. We construct a model of the underlying asset price process which is dynamically consistent to the power law. The volatility process of the model is driven by a fractional Brownian motion with Hurst parameter less than half. The fractional Brownian motion is correlated with a Brownian motion which drives the asset price process. We derive an asymptotic expansion of the implied volatility as the time to maturity tends to zero. For this purpose, we introduce a new approach to validate such an expansion, which enables us to treat more general models than in the literature. The local-stochastic volatility model is treated as well under an essentially minimal regularity condition in order to show such a standard model cannot be dynamically consistent to the power law.     

Can negative interest rates really affect option pricing? Empirical evidence from an explicitly solvable stochastic volatility model

The profound financial crisis generated by the collapse of Lehman Brothers and the European sovereign debt crisis in 2011 have caused negative values of government bond yields both in the USA and in the EURO area. This paper investigates whether the use of models which allow for negative interest rates can improve option pricing and implied volatility forecasting. This is done with special attention to foreign exchange and index options. To this end, we carried out an empirical analysis on the prices of call and put options on the US S&P 500 index and Eurodollar futures using a generalization of the Heston model in the stochastic interest rate framework. Specifically, the dynamics of the option’s underlying asset is described by two factors: a stochastic variance and a stochastic interest rate. The volatility is not allowed to be negative, but the interest rate is. Explicit formulas for the transition probability density function and moments are derived. These formulas are used to estimate the model parameters efficiently. Three empirical analyses are illustrated. The first two show that the use of models which allow for negative interest rates can efficiently reproduce implied volatility and forecast option prices (i.e. S&P index and foreign exchange options). The last studies how the US three-month government bond yield affects the US S&P 500 index.     

Geometric Asian option pricing in general affine stochastic volatility models with jumps

In this paper, we present some results on Geometric Asian option valuation for affine stochastic volatility models with jumps. We shall provide a general framework into which several different valuation problems based on some average process can be cast, and we shall obtain closed form solutions for some relevant affine model classes.     

Remittance,Institutions and Investment Volatility Interactions: An Intercontinental Analysis

Generating massive investment for growth and development has been one of the main policy goals of most economies around the globe. Countries, most especially developing ones, are highly susceptible to investment volatility owing largely to the fragile nature of their economies as well as weaknesses in terms of dysfunctional institutions. Therefore, sound economic management suggests the need to better understand possible sources for mitigating the adverse effects of investment volatility. Remittances have been identified as important capital flows which do a good job of dousing macroeconomic volatilities. It is on this basis that the study sought to uncover the causal relationship between remittances and investment volatility via the intermediating role of institutions. Using a panel of 70 countries and the system Generalized Method of Moments (GMM) estimator, three insightful outcomes come to the fore. First, remittances played countercyclical roles across the estimated regressions. Second, institutional quality had no significant role in mitigating investment volatility and lastly, the interactive terms of both remittances and institutions significantly mitigated the negative impacts of investment volatility with the exception of the political component of the institutional architecture. Policy suggestions are drawn based on our results.     

Modeling the time varying volatility of housing returns: Further evidence from the U.S. metropolitan condominium markets

This study extends the literature on modeling the volatility of housing returns to the case of condominium returns for five major U.S. metropolitan areas (Boston, Chicago, Los Angeles, New York, and San Francisco). Through the estimation of ARMA models for the respective condominium returns, we find volatility clustering of the residuals. The results from an ARMA‐TGARCH‐M model reveal the absence of asymmetry in the conditional variance. Dummy variables associated with the housing market collapse unique to each metropolitan area were statistically insignificant in the conditional variance equation, but negative and statistically significant in the mean equation. Condominium markets in Los Angeles and San Francisco exhibit the greatest persistence to volatility shocks.